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Concentration inequalities quantify the deviation of a random variable from a fixed value. In spite of numerous applications, such as opinion surveys or ecological counting procedures, few concentration results are known for the setting of…

Statistics Theory · Mathematics 2015-07-28 Rémi Bardenet , Odalric-Ambrym Maillard

Known Bernstein-type upper bounds on the tail probabilities for sums of independent zero-mean sub-exponential random variables are improved in several ways at once. The new upper bounds have a certain optimality property.

Probability · Mathematics 2022-08-15 Iosif Pinelis

We prove an invariance principle for non-stationary random processes and establish a rate of convergence under a new type of mixing condition. The dependence is exponentially decaying in the gap between the past and the future and is…

Probability · Mathematics 2024-12-23 Ion Grama , Émile Le Page , Marc Peigné

In this paper, we show that the empirical measure of mean-field model satisfies the large deviation principle with respect to the weak convergence topology or the stronger Wasserstein metric, under the strong exponential integrability…

Probability · Mathematics 2019-02-20 Wei Liu , Liming Wu

This paper extends classical probabilistic results to the broader class of demimartingales and demisubmartingales. We establish variants of Doob's-type optional sampling theorem under minimal structural conditions on stopping times, relying…

Probability · Mathematics 2025-07-24 Milto Hadjikyriakou , B. L. S Prakasa Rao

In this article we present a Bernstein inequality for sums of random variables which are defined on a spatial lattice structure. The inequality can be used to derive concentration inequalities. It can be useful to obtain consistency…

Statistics Theory · Mathematics 2017-12-06 Eduardo Valenzuela-Domínguez , Johannes T. N. Krebs , Jürgen E. Franke

The aim of this paper is to establish Hoeffding and Bernstein type concentration inequalities for weighted sums of exchangeable random variables. A special case is the i.i.d. setting, where random variables are sampled independently from…

Statistics Theory · Mathematics 2025-08-11 Rina Foygel Barber

In this work, Bernstein's concentration inequalities for squared integrable matrix-valued discrete-time martingales are obtained. Based on Lieb's theory and Bernstein's condition, a suitable supermartingale can be constructed. Our proof is…

Probability · Mathematics 2021-03-26 Zijie Tian

Bernstein's inequality is a central result in the theory of $D$-modules on smooth varieties. While Bernstein's inequality fails for rings of differential operators on general singularities, recent work of \`{A}lvarez Montaner, Hern\'andez,…

Commutative Algebra · Mathematics 2024-03-21 Jack Jeffries , David Lieberman

We consider the problem of bounding large deviations for non-i.i.d. random variables that are allowed to have arbitrary dependencies. Previous works typically assumed a specific dependence structure, namely the existence of independent…

Probability · Mathematics 2018-11-06 Christoph H. Lampert , Liva Ralaivola , Alexander Zimin

We prove a large deviation principle for the sequence of push-forwards of empirical measures in the setting of Riesz potential interactions on compact subsets K in R^d with continuous external fields. Our results are valid for base measures…

Classical Analysis and ODEs · Mathematics 2016-10-27 Tom Bloom , Norman Levenberg , Franck Wielonsky

A moderate deviations principle for the law of a stochastic Burgers equation is proved via the weak convergence approach. In addition, some useful estimates toward a central limit theorem are established.

Probability · Mathematics 2020-01-17 Rachid Belfadli , Lahcen Boulanba , Mohamed Mellouk

This paper proves a Berry--Esseen theorem for sample quantiles of strongly-mixing random variables under a polynomial mixing rate. The rate of normal approximation is shown to be $O(n^{-1/2})$ as $n\to\infty$, where $n$ denotes the sample…

Probability · Mathematics 2009-03-02 S. N. Lahiri , S. Sun

We show that the Bernstein-Hoeffding method can be employed to a larger class of generalized moments. This class includes the exponential moments whose properties play a key role in the proof of a well-known inequality of Wassily Hoeffding,…

Probability · Mathematics 2015-09-02 Christos Pelekis , Jan Ramon , Yuyi Wang

Grouped data are commonly encountered in applications. The Bernstein polynomial model is proposed as an approximate model in this paper for estimating a univariate density function based on grouped data. The coefficients of the Bernstein…

Methodology · Statistics 2015-07-21 Zhong Guan

In this paper, we establish a moderate deviations principle for the Langevin dynamics with strong damping. The weak convergence approach plays an important role in the proof.

Probability · Mathematics 2018-02-05 Lingyan Cheng , Ruinan Li , Wei Liu

Consider the stochastic differential equation in $\rr^d$ dX^{\e}_t&=b(X^{\e}_t)dt+\sqrt{\e}\sigma(X^\e_t)dB_t X^{\e}_0&=x_0,\quad x_0\in\rr^d$ where $b:\rr^d\to\rr^d$ is $C^1$ such that $<x,b(x)> \leq C(1+|x|^2)$, $\sigma:\rr^d\to…

Probability · Mathematics 2026-04-14 Yutao ma , Ran Wang , Liming Wu

We give improved constants for data dependent and variance sensitive confidence bounds, called empirical Bernstein bounds, and extend these inequalities to hold uniformly over classes of functionswhose growth function is polynomial in the…

Machine Learning · Statistics 2009-07-23 Andreas Maurer , Massimiliano Pontil

We obtain non-uniform Berry-Esseen type estimates for several classes of weakly dependent sequences of random variables, including uniformly elliptic inhomogeneous Markov chains, random and time-varying (partially) hyperbolic or expanding…

Probability · Mathematics 2026-05-12 Yeor Hafouta

In this paper, we give precise rates of convergence in the strong invariance principle for stationary sequences of bounded real-valued random variables satisfying weak dependence conditions. One of the main ingredients is a new Fuk-Nagaev…

Probability · Mathematics 2023-07-06 J Dedecker , F Merlevède , Emmanuel Rio